IIUM Wifi Quality Survery

Introduction

Some text for introduction to this survey and results.

Results

Data Preparation

Student survey has been conducted for 2 weeks and received 51 total reponses.

Load all required libraries that will be used throughout this analysis.

library(plyr)
library(psych)
library(plotly)
library(summarytools)

st_options(plain.ascii = FALSE, style = "rmarkdown", dfSummary.varnumbers = FALSE, dfSummary.valid.col = FALSE)

Start reading both CSV files, from student survey and speedtest result.

csv_surv <- read.csv('Student_Survey.csv')
csv_test <- read.csv('Speedtest_Result.csv')

Rename variables name to something more meaningful, shorter and easier to code.

tmpstud <-
  plyr::rename(
    csv_surv,
    c(
      Academic.Programme = "Programme",
      Internet.Connection.Method = "SSID",
      Main.Usage.of.Internet = "Usage",
      On.overall.basis..how.satisfied.are.you.with.wireless.internet.connection.provided.by.IIUM. = "Satisfaction",
      Propose.a.solution.to.improve.IIUM.WIFI.services. = "Comment",
      Average.Duration.of.Daily.Use.of.Internet = "Duration",
      How.many.times.have.your.Internet.connection.been.suddenly.disconnected.or.face.intermittent.connection.in.past.3.days. = "Disconnected",
      Is.WiFi.coverage.adequate.and.within.acceptable.signal.strength. = "Coverage",
      How.satisfied.are.you.with.the.network.speed.to.achieve.your.main.usages. = "Speed"
    )
  )
tmptest <-
  plyr::rename(
    csv_test,
    c(
      Mahallah.Dept = "Mahallah",
      Download.Mbps = "Download",
      Upload.Mbps = "Upload",
      Ping.ms = "Ping",
      Jitter.ms = "Jitter",
      Loss.. = "Loss"
    )
  )

Perform data cleansing

tmpstud <- tmpstud[which(tmpstud$Gender == ''),]
tmpstud$Timestamp <- tmpstud$Gender <- tmpstud$Comment <- NULL
tmpstud$Usage[7] <-
  "Study and assignment, Topic and subject research, Casual browsing"
tmpstud$Usage <- strsplit(as.character(tmpstud$Usage), ",")
tmpstud$Satisfaction <-
  ifelse(
    tmpstud$Satisfaction == 1,
    "Very Dissatisfied",
    ifelse(
      tmpstud$Satisfaction == 2,
      "Dissatisfied",
      ifelse(tmpstud$Satisfaction == 3, "Satisfied", "Very Satisfied")
    )
  )
tmpstud$Speed <-
  ifelse(
    tmpstud$Speed == 1,
    "Very Dissatisfied",
    ifelse(
      tmpstud$Speed == 2,
      "Dissatisfied",
      ifelse(tmpstud$Speed == 3, "Satisfied", "Very Satisfied")
    )
  )

tmptest$Loss <- 0

Split Usage into list and transpose into dataframe

k <- 1

Respondent <- character()
Programme <- character()
SSID <- character()
Mahallah <- character()
Usage <- character()
Satisfaction <- character()
Duration <- character()
Speed <- character()
Disconnected <- character()
Coverage <- character()

# iterate each row to split Usage into separate line in data frame
for (i in 1:nrow(tmpstud))
{
  for (j in 1:3)
  {
    Respondent[k] <- i # to identify unique number of respondent
    Programme[k] <- as.character(tmpstud$Programme[i])
    SSID[k] <- as.character(tmpstud$SSID[i])
    Mahallah[k] <- as.character(tmpstud$Mahallah[i])
    Usage[k] <-
      trimws(tmpstud$Usage[[i]][j]) # trim leading and trailing whitespace
    Satisfaction[k] <- as.character(tmpstud$Satisfaction[i])
    Duration[k] <- as.character(tmpstud$Duration[i])
    Speed[k] <- as.character(tmpstud$Speed[i])
    Disconnected[k] <- as.character(tmpstud$Disconnected[i])
    Coverage[k] <- as.character(tmpstud$Coverage[i])
    
    k <- k + 1
  }
}

Finalized dataframe from previous step

dfstudentB <-
  data.frame(
    Respondent,
    Programme,
    SSID,
    Mahallah,
    Usage,
    Satisfaction,
    Duration,
    Speed,
    Disconnected,
    Coverage,
    stringsAsFactors = TRUE
  )
dfstudentA <-
  unique(dfstudentB[,-5]) # minus Usage and retrieve only unique records

dftest <- tmptest

High level summary

dfSummary(
  dfstudentA[,-1],
  max.distinct.values = 15,
  plain.ascii = FALSE,
  style = "grid",
  graph.magnif = 0.75,
  valid.col = FALSE,
  tmp.img.dir = "./img",
  na.col = FALSE
)

Data Frame Summary

dfstudentA

Dimensions: 50 x 8
Duplicates: 1

Variable	Stats / Values	Freqs (% of Valid)	Graph
Programme [factor]	1. Alumni (<= 2 Years) 2. Postgraduate (PG) 3. Undergraduate (UG)	9 (18.0%) 7 (14.0%) 34 (68.0%)
SSID [factor]	1. WiFi - Guest 2. WiFi - Staff 3. WiFi - Student	1 ( 2.0%) 1 ( 2.0%) 48 (96.0%)
Mahallah [factor]	1. Ali 2. Ameenah 3. As-Siddiq 4. Asiah 5. Hafsah 6. Halimatus Saa’diah 7. Living outside IIUM 8. Maryam 9. Nusaibah 10. Ruqayyah 11. Safiyyah 12. Salahuddin 13. Sumayyah 14. Uthman 15. Zubair	2 ( 4.0%) 2 ( 4.0%) 2 ( 4.0%) 2 ( 4.0%) 3 ( 6.0%) 1 ( 2.0%) 4 ( 8.0%) 1 ( 2.0%) 4 ( 8.0%) 1 ( 2.0%) 8 (16.0%) 4 ( 8.0%) 2 ( 4.0%) 4 ( 8.0%) 10 (20.0%)
Satisfaction [factor]	1. Dissatisfied 2. Satisfied 3. Very Dissatisfied 4. Very Satisfied	20 (40.0%) 15 (30.0%) 14 (28.0%) 1 ( 2.0%)
Duration [factor]	1. 1 - 4 hours 2. 13 - 18 hours 3. 5 - 8 hours 4. 9 - 12 hours 5. Less than 1 hour 6. More than 18 hours	7 (14.0%) 5 (10.0%) 18 (36.0%) 13 (26.0%) 3 ( 6.0%) 4 ( 8.0%)
Speed [factor]	1. Dissatisfied 2. Satisfied 3. Very Dissatisfied 4. Very Satisfied	22 (44.0%) 13 (26.0%) 11 (22.0%) 4 ( 8.0%)
Disconnected [factor]	1. 1 - 5 times 2. 6 - 10 times 3. More than 10 times 4. Never	18 (36.0%) 7 (14.0%) 17 (34.0%) 8 (16.0%)
Coverage [factor]	1. No 2. Yes	33 (66.0%) 17 (34.0%)

Usage of Internet

dfusage <- count(dfstudentB$Usage)
plot_ly(
  dfusage,
  labels = dfusage$x,
  values = ~ dfusage$freq,
  type = 'pie'
) %>%
  layout(
    title = 'Primary Usage of Internet',
    xaxis = list(
      showgrid = FALSE,
      zeroline = FALSE,
      showticklabels = FALSE
    ),
    yaxis = list(
      showgrid = FALSE,
      zeroline = FALSE,
      showticklabels = FALSE
    )
  )

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